Miscanthus is a perennial rhizomatous C4 grass native to East Asia. Endowed with great biomass yield, high ligno-cellulose composition, efficient use of radiation, nutrient and water, as well as tolerance to stress, Miscanthus has great potential as an excellent bioenergy crop. Despite of the high potential for biomass production of the allotriploid hybrid M. ×giganteus, derived from M. sacchariflorus and M. sinensis, other options need to be explored to improve the narrow genetic base of M. ×giganteus, and also to exploit other Miscanthus species, including M. sinensis (2n = 2x = 38), as bioenergy crops. In the present study, a large number of 459 M. sinensis accessions, collected from the wide geographical distribution regions in China, were genotyped using 23 SSR markers transferable from Brachypodium distachyon. Genetic diversity and population structure were assessed. High genetic diversity and differentiation of the germplasm were observed, with 115 alleles in total, a polymorphic rate of 0.77, Nei’s genetic diversity index (He) of 0.32 and polymorphism information content (PIC) of 0.26. Clustering of germplasm accessions was primarily in agreement with the natural geographic distribution. AMOVA and genetic distance analyses confirmed the genetic differentiation in the M. sinensis germplasm and it was grouped into five clusters or subpopulations. Significant genetic variation among subpopulations indicated obvious genetic differentiation in the collections, but within-subpopulation variation (83%) was substantially greater than the between-subpopulation variation (17%). Considerable phenotypic variation was observed for multiple traits among 300 M. sinensis accessions. Nine SSR markers were found to be associated with heading date and biomass yield. The diverse Chinese M. sinensis germplasm and newly identified SSR markers were proved to be valuable for breeding Miscanthus varieties with desired bioenergy traits.

Ginkgo biloba L. is one of the most extensively planted and productive commercial species in temperate areas around the world, but slow-growth is the most limiting factor for its utilization. Fertilization is one of the key technologies for high quality and high forest yield. To better understand the impacts of fertilization on Ginkgo productivity, the effects of fertilization treatments (single fertilizer and combined fertilizer) on growth, nutrient content in Ginkgo leaves, and photosynthesis characteristics were studied in a 10-year-old Ginkgo plantation over two years. The single factor experiments suggested that DBH (diameter at breast height), H (height), NSL (length of new shoots), and V (trunk volume) showed significant differences between the different levels of single nitrogen (N) or phosphate (P) fertilizer application. Orthogonal test results showed that the nine treatments all promoted the growth of Ginkgo, and the formula (N: 400 g·tree −1 , P: 200 g·tree −1 , potassium (K): 90 g·tree −1 ) was the most effective. G s (stomatal conductance) and P n (net photosynthesis rate) showed significant differences between the different amounts of single N or P fertilizer application, while single K fertilizer only affected P n . Combined N, P, and K fertilizer had significant promoting effects on C i (intercellular CO 2 concentration), G s and P n . N and P contents in Ginkgo leaves showed significant differences between the different amounts of a single N fertilizer application. A single P fertilizer only improved foliar P contents in Ginkgo leaves. A single K fertilizer application improved N and K content in Ginkgo leaves. The effects of different N, P, and K fertilizer treatments on the nutrient content of Ginkgo leaves were different.

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R2 = 0.9928; testing: R2 = 0.9928), SSC (training: R2 = 0.9749; testing: R2 = 0.9143) and firmness (training: R2 = 0.9814; testing: R2 = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.